The present invention relates to a method for visualizing zones of higher activity in a surveillance scene monitored by at least one surveillance apparatus, and also to a corresponding surveillance system.
Surveillance systems serve to monitor relevant regions and usually comprise a multiplicity of surveillance apparatuses, for example surveillance cameras which are provided at the relevant regions for recording surveillance scenes. Alternatively, or in addition thereto, use can for example also be made of laser-supported or infrared-supported motion sensors, such as e.g. motion detectors or photoelectric sensors. Stepping plates which trigger a signal when stepped on should be mentioned as a further example. By way of example, the surveillance scenes can comprise parking lots, crossroads, roads, squares, and also regions in buildings, factories, hospitals or the like. The data, for example image data feeds, recorded by the surveillance apparatuses are collected in a surveillance center, where these are evaluated either in an automatic fashion or by surveillance staff.
The manual evaluation of image data feeds in particular can, under certain circumstances, be very difficult because surveillance scenes with a multiplicity of moving objects in particular, e.g. street scenes, require a corresponding large number of moving objects to be monitored and this may also be due to the fact that the image quality of the displayed surveillance scenes is often unsatisfactory, for example as a result of changes in illumination, environmental influences or dirtying of the surveillance cameras. Surveillance scenes usually monitored contain three-dimensional objects, with movement processes often being covered by the objects and hence not being accessible to direct surveillance. It is therefore expedient for there to be electronic support for the surveillance staff.
It is common practice to display surveillance scenes captured by multi-camera networks as three-dimensional models (referred to as 3D scene models below). Such 3D scene models can have an arbitrary degree of abstraction; for example, it is possible to display surfaces of monitored houses, signals, streets and the like as abstracted 2D structures. A 3D scene model can also have an abstracted, grid-shaped reproduction of three-dimensional objects, and so it becomes possible to identify the objects which were captured by the surveillance apparatuses but are covered in the current view of the 3D scene model. Such 3D scene models offer the option of interactive navigation, for example by means of appropriate computer systems.
In this context, DE 10 2007 048 857 A1 has disclosed a method which allows a three-dimensional scene model to be equipped with realistic textures, with it being possible for the textures to be updated at regular or irregular intervals.
WO 2009/068336 A2 has disclosed model-based 3D position determination of an object. To this end, the object is recorded by a calibrated camera, with the 3D position of the object being determined as intersection of the line of sight with the scene model.
The article “Modeling and Visualization of Human Activities for Multi-Camera Networks” by Sankaranarayanan et al., filed in July 2009 (EURASIP Journal on Image and Video Processing), discloses a system by means of which it is possible to project person movements, indentified in images, into 3D scene models. By using a priori knowledge of the 3D structure of the scene and the camera calibration, the system is able to localize persons who are moving through the scene. Activities of interest are determined by virtue of the fact that models of these activities are compared to observations within the scope of a self-learning method.
In “Correspondence-Free Multi-Camera Activity Analysis and Scene Modeling” (Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2008), Wang et al. describe the combination of aligned movements, as a result of which zones of higher activity are even to be displayed in overcrowded scenes. However, this does not comprise the use of a 3D scene model.
In the methods known from the prior art, the display of individual trajectories in the case of a multiplicity of moving objects often leads to great complexity and, ultimately, to less information content. Movements covered by objects or merely captured by individual cameras are often impossible to capture. Moreover, if erroneous object correspondences are present, i.e. if there is not absolutely exact camera matching, this often leads to a strong deterioration in the quality of the trajectories.
There therefore still is the need for methods for visualizing zones of higher activity, particularly in multi-camera networks, which overcome the aforementioned disadvantages of the prior art.